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Machine Learning Architect (SAS Viya) (8 months contract) at George Bernard Consulting · web2uaApply on the original site
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- Define and own the end-to-end machine learning architecture for financial and taxation platforms
- Design scalable, secure, and compliant ML solutions using SAS Viya and complementary ML technologies
- Establish architectural standards, best practices, and governance frameworks for enterprise ML systems
- Lead the design of data ingestion, feature engineering, model training, deployment, and monitoring pipelines
- Ensure ML solutions comply with regulatory, audit, data privacy, and risk management requirements
Define and enforce MLOps standards including model lifecycle management, versioning, explainability, and performance monitoring
Collaborate with finance, taxation, compliance, and risk stakeholders to translate business and regulatory needs into technical solutionsReview and approve ML designs, pipelines, and deployment strategies across teamsEvaluate and introduce new ML technologies and platforms aligned with enterprise and regulatory needsProvide technical leadership and mentorship to senior ML engineers and teamsRequirements
- Bachelors or Masters degree in Computer Science, Data Science, Engineering, Statistics, or a related field
- 8+ years of experience in data, analytics, or machine learning roles, with at least 4+ years in architecture or technical leadership positions
- Strong domain experience in financial services, taxation, risk, or regulatory analytics
- Extensive hands-on and architectural experience with SAS Viya
- Deep understanding of machine learning algorithms, statistical modeling, and financial data analytics
- Strong expertise in Python and ML libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch
- Proven experience with MLOps practices, model governance, explainable AI, and risk controls
- Experience designing and deploying ML solutions on cloud platforms such as AWS, Azure, or GCP
- Strong SQL skills and experience working with large-scale financial datasets
- Knowledge of big data or distributed processing frameworks such as Spark is an advantage
- Originally posted on Himalayas
